Platform · Regional food aggregators

Regional Food Aggregator Data

Q: How do you know whether the regional platform adds anything?

coverage_tier and also_on_global_platform make the overlap visible — high in metros, falling outside them. If the overlap is high everywhere in your scope, the regional platform is adding cost and little coverage, and we will say so.

Global platforms cover the metros. The regional operator is where the rest of a market's merchants are, and the gap is not random.

Regional food aggregator data covers the delivery platforms that hold strong positions in specific markets alongside or instead of the global operators. The reason to collect them is merchant coverage: global platforms concentrate where density supports the economics, so a global-only panel is thin outside the major metros and reads that thinness as absence.

The same argument our Demae-can page makes about Japan applies in market after market, which is why this is one page rather than five.

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regional_aggregators.jsonl LIVE FEED

{"platform":"regional-a","market":"market-1", "merchant_id":"rf-44120","city":"Example secondary city", "coverage_tier":"secondary", "also_on_global_platform":false, "item_price":42.00,"price_setter":"merchant", "min_realisable_price":48.00, "note":"not on the global platform at all. a global-only panel reads this city as empty"} {"coverage_tier":"metro","also_on_global_platform":true, "item_price":39.00, "global_platform_price":44.00, "note":"same merchant, both platforms. commission differs, so markup differs"} {"markets_observed":["market-1","market-2","market-4"], "market_3_status":"absent_this_batch", "caution":"recorded per batch, not assumed. a withdrawal shows as structural, not as lost coverage"}

3 of 4,204,110 merchant-item rows · multi-marketcoverage_tier on every merchant · schedules matched · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Regional food aggregators or its owners. Regional food aggregators and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Regional food aggregators at a glance

How we handle Regional food aggregators specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Scope: Regional delivery aggregators across several markets
Why one page: The mechanics are identical to any marketplace. Coverage is the reason
The argument: Global platforms concentrate in metros
Consequence: A global-only panel is thin outside them, and reads thin as absent
What we report: coverage_tier per merchant, so panel thinness is visible
Price setter: Merchant, as on any marketplace
Overlap: High in metros. The value is in the rows outside them
Refresh: Daily standard; matched to the global platform's schedule for comparability

Platform specifics

Coverage is the argument, not mechanics

These are the reasons a Regional food aggregators dataset needs its own handling rather than a shared retail schema.

Where the global platforms stop

Global delivery platforms operate where merchant density and order volume support courier economics. That is rational and it produces a predictable coverage shape:

Deep in major metros, where a global-only panel is genuinely representative.
Thinner in secondary cities, where fewer merchants are on-platform.
Thin or absent beyond that, where a regional operator may hold the market.

The analytical problem is that a panel cannot distinguish "few merchants here" from "few merchants on this platform here" without a second source. A brand reading a global-only panel concludes a secondary city has little delivery activity when it has a different platform.

What we record

coverage_tier on every merchant — metro, secondary or beyond — and also_on_global_platform where both are in scope. That makes the overlap visible: high in metros, and falling outside them, which is exactly where the regional operator earns its cost.

Mechanics are the same as any marketplace, and we say so

There is nothing structurally novel about a regional aggregator. Everything our Uber Eats page sets out applies:

The merchant sets the price, frequently above their own dine-in price to cover commission.
Modifier groups make the headline item price not the entry price, so minimum realisable price is the number to compare on.
Fees are separate fields — delivery, service, small order.
Availability is address-level, since merchant reach is radius-based.
No sales, order volumes, courier or customer data. None of it is published.

Separate pages per aggregator would restate that five times. The coverage argument is what differs, and it is the same argument in every market — which is why this is one page.

Where the per-market detail lives

Markets with their own specifics already have a page: Demae-can for Japan's nationwide reach, Keeta and HungerStation for the Gulf, and iFood for Brazil's regional price variation.

Schedule alignment, and one thing worth watching

Schedules must match the global platform

The point of adding a regional aggregator is comparison, and a comparison between platforms observed at different times is not a comparison. We hold the schedule identical across platforms in a market.

That matters more than usual here because merchant availability moves through the day, and a regional operator observed at a different hour will appear to have different coverage for reasons that are purely scheduling.

Merchant overlap is a finding in itself

Where a merchant appears on both a global and a regional platform, the price is frequently different — commission rates differ, so the markup differs.

We record both as separate records against the same merchant identity where we can establish it. The gap is an observation you compute, not a margin we assert.

Market exits

Regional platforms consolidate. As on our foodpanda page, we record markets_observed per batch rather than working from a fixed list, so a withdrawal appears as a structural change rather than a silent drop in coverage.

Scope

What we collect on Regional food aggregators, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

coverage_tier on every merchant — metro, secondary or beyond
also_on_global_platform where both are in scope
markets_observed recorded per batch, never assumed from a list
price_setter recorded as merchant
Modifier groups with minimum realisable price
Delivery, service and small-order fees as separate fields
Identical observation schedule across platforms in a market
Local-language item names retained exactly as published
Merchant identity matched across platforms where establishable

❌ What we do not, and why

A global-only panel presented as market coverage
Platforms compared on different observation schedules
A markup inferred where only one platform is observed
A fixed market list assumed rather than observed
Sales, order volumes, courier or customer data

Core Regional food aggregators fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
platform / market / markets_observed	Which platform, which market, and what the batch saw
merchant_id / merchant_name / city	A commercial entity, and where
coverage_tier	metro, secondary or beyond
also_on_global_platform	Where both are in scope
item_name_local / language_dominant	Original text and its language
item_price / currency / price_setter	Price, and that the merchant set it
modifier_groups / min_realisable_price	The number a customer can pay
delivery_fee / service_fee / small_order_fee	Each separately
merchant_open / item_available	Two distinct states
observation_interval_minutes	So platform comparison is valid
observed_at / daypart	Timestamp and derived daypart

Use cases

What teams do with Regional food aggregators data

Merchant coverage beyond the metros

coverage_tier per merchant, so a panel distinguishes few merchants in an area from few merchants on one platform in that area — which a global-only panel cannot do.

Cross-platform merchant markup

The same merchant on a global and a regional platform as separate records, so the price difference driven by differing commission rates is an observation rather than an assumption.

Market structure monitoring

markets_observed per batch, so a regional platform withdrawing from a market appears as a structural change rather than a silent drop in coverage.

National brand delivery presence

A chain's listings across both global and regional platforms, which is the only way to see its actual delivery footprint in a market where the two split geographically.

The 24-hour sample — run on your sources, not ours

Send us a Regional food aggregators item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Regional food aggregators is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Regional food aggregators data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what food & restaurant data covers, and a Regional food aggregators-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Regional food aggregators data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Coverage. Global platforms operate where density supports courier economics, so they are deep in metros and thin outside them.

A global-only panel cannot distinguish 'few merchants here' from 'few merchants on this platform here'. A brand reading one concludes a secondary city has little delivery activity when it has a different platform.

No, and we say so rather than inventing a difference. The merchant sets the price, modifiers make the headline not the entry price, fees are separate, availability is address-level, and no sales data is published.

Separate pages per aggregator would restate that five times. The coverage argument is what differs, and it is the same argument in every market.

coverage_tier and also_on_global_platform make the overlap visible — high in metros, falling outside them.

If the overlap is high everywhere in your scope, the regional platform is adding cost and little coverage, and we will say so.

Because the point of adding a platform is comparison, and a comparison between platforms observed at different times is not one.

It matters more here than usual because merchant availability moves through the day — a platform observed at a different hour appears to have different coverage for purely scheduling reasons.

Frequently, because commission rates differ so the merchant's markup differs. We record both as separate records against the same merchant identity where we can establish it.

The gap is an observation you compute from two records, not a margin we assert.

Less than the first platform in a market, since the schema and merchant matching are already built. Market count is the driver rather than merchant volume.

One scoping call, a free pilot within 24 hours including the overlap by coverage tier, then a fixed monthly quote. Request a quote.

See real Regional food aggregators data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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